A Review of the Research of Quick Access Recorder data
DOI:
https://doi.org/10.54097/ajst.v5i1.5395Keywords:
QAR data, Aircraft engine, Fuel.Abstract
In recent years, QAR data has been widely concerned by scholars because of its reliability and integrity. QAR data has become an important basis for flight quality monitoring, engine status detection, aircraft system failure diagnosis, 3D animation route design and other aspects of various airlines in the world. The keywords of research on QAR data in China and other countries were clustering analyzed by VOSviewer, the hot spots were introduced, and research were summarized and discussed from five aspects. At last, some shortcomings of current research on QAR data were pointed out and some future development directions were presented.
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References
CAO Huiling, ZHOU Baizheng. Application Research of QAR Data on Aero-Engine Monitoring [J]. Journal of Civil Aviation University of China, 2010, 28(03): 15-19.
SHI Xiu-yu. Fault Diagnosis Approach of Performance for Civil Aeroengine[J]. Aeroengine, 2008(03): 49-51.
WANG Yiwei, MO Liping, WANG Yishou,et al. Aero-engine status identification based on full-segment QAR data and convolutional nerural network[J]. Journal of Aerospace Power, 2021, 36(07): 1556-1563.
WANG Yi-shou, YU Ying-hong, QING Xin-lin, et al. Exhaust Gas Temperature Baseline Model of Aeroengine Based on Kernel Principal Component Analysis [J]. Aeroengine, 2020, 46(01): 54-60.
SHI Hongwei. Implementation and development of fault monitoring by airlines using ACMS and QAR data [J]. Aviation Maintenance & Engineering, 2021(02): 18-20.
CAO Huiling, LIN Dajin, ZENG Xuefeng. Declining Formula Establish and Analysis of CFM56-7B Aero-Engine [J]. Journal of Civil Aviation University of China, 2010, 28(04): 9-12.
Cao Huiling, Yang Lu, Lin Yusen, et al. Aero-engine Anomaly Detection Using Support Vector Regression [J]. Mechanical Science and Technology for Aerospace Engineering, 2013, 32(11): 1616-1619.
CAO Huiling,HUANG Leteng,KANG Liping. Research on engine health assessment based on grey correlation analysis method [J]. Mathematics In Practice And Theory, 2015, 45(02): 122-129.
Gao X L, Zuo H F, Sun J Z, et al. Civil Aircraft Engine Start System Health Monitoring Method Based on QAR Data[M]. New York: Ieee, 2017: 168-173.
Xu Y J, Hou W K, Li W Z, et al.: Aero-Engine Gas-path Fault Diagnosis Based on Spatial Structural Characteristics of QAR Data, 2018 Annual Reliability and Maintainability Symposium, New York: Ieee, 2018.
CAO Huiling, ZHANG Hao. Fatigue Life Prediction of CFM56-7B Engine High Pressure Turbine Blades [J]. Aviation Maintenance & Engineering, 2021(07): 89-91.
HUANG Lei, GAO Shuwei. Load Spectrum Prediction of Engine High Pressure Turbine Blade Based on Numerical Simulation [J]. Electronic Technology, 2020, 49(03): 120-121.
Liu J Q, Feng Y W, Lu C, et al. Vibration Reliability Analysis of Aeroengine Rotor Based on Intelligent Neural Network Modeling Framework[J]. Shock and Vibration, 2021, 2021: 11.
TAN Yan, WEI Wuguo. Research on Working Baseline of Adjustable Discharge Valve of Aeroengine Based on Support Vector Regression [J]. Mathematics In Practice And Theory, 2020, 50(12): 22-27.
FENG Xiao, ZHANG Enyi. Fault Analysis and Status Monitoring of LEAP-1A Engine Starting Valve Based on QAR Data [J]. Aviation Maintenance & Engineering, 2021(05): 57-59.
Tan Y, Iop. Fitting Operation Curve of Civil Aviation Turbo-fan Engine's Variable Bleed Valve based on MATLAB[C]. International Seminar on Computer Science and Engineering Technology (SCSET), 2018.
Zhang M, Huang Q W, Liu S H, et al. Fuel Consumption Model of the Climbing Phase of Departure Aircraft Based on Flight Data Analysis[J]. Sustainability, 2019, 11(16): 23.
Oh E M, Kim H, Jeon D, et al.: A Model for Estimation of Fuel Consumption during Aircraft Taxi Operations, 2018 Ieee/Aiaa 37th Digital Avionics Systems Conference, New York: Ieee, 2018: 266-271.
CAO Huiling, JIA Chao. Research on fuel flow control law of civil aviation engine based on QAR [J]. Science Technology and Engineering, 2013, 13(13): 3814-3817+3827.
Wang K, Chen J J, Ieee. An Interval Prediction Method for Imbalanced Fuel Consumption Data[C]. Chinese Automation Congress (CAC), 2020: 824-829.
Wu Z X, Luo W Z, Chen C, et al. Research on Influencing Factors of Fuel Flow Based on QAR Data[M]. New York: Ieee, 2020: 800-804.
Ye L S, Cao L, Wang X H. EVALUATING FUEL CONSUMPTION FOR CONTINUOUS DESCENT APPROACH BASED ON QAR DATA[J]. Promet-Traffic & Transportation, 2019, 31(4): 407-421.
Wang H, Zuo H F, Sun J Z, et al. Research on On-line Monitoring Method of Lubricating Oil Consumption Rate of Aeroengine Based on QAR Data[M]. New York: Ieee, 2017: 191-197.
WU Ren-biao, CHEN Bin, SUN Shu-guang,et al. Fault diagnosis of airborne equipment simulationsystems based on slide window detection[J]. Journal of Civil Aviation University of China, 2012, 30(01): 1-5.
Li C Y, Sun J Z, Zuo H F, et al. Fault Detection for Air Conditioning System of Civil Aircraft Based on Multivariate State Estimation Technique[M]. New York: Ieee, 2017: 180-185.
C. Edward LANa, WU Kaiyuanb, YU Jiang. Flight Characteristics Analysis Based on QAR Data of a Jet Transport During Landing at a High-altitude Airport [J]. Chinese Journal of Aeronautics, 2012, 25(01): 13-24.
Wang L X, Qian Y, Chen X G. Research on QAR Outliers Processing Based on Kalman Filtering and Newton Interpolation Algorithms[M]. New York: Ieee, 2020: 805-808.
SUN Rui-shan, YANG Yi-xuan. Research on Extraction of Key Parameters During Take-off Based on QAR Data [J]. China Transportation Review, 2015, 37(09): 58-63.
Uzun M, Demirezen M U, Koyuncu E, et al.: Deep Learning Techniques for Improving Estimations of Key Parameters for Efficient Flight Planning, 2019 Ieee/Aiaa 38th Digital Avionics Systems Conference, New York: Ieee, 2019.
Wang L, Ren Y, Wu C X. Effects of flare operation on landing safety: A study based on ANOVA of real flight data[J]. Safety Science, 2018, 102: 14-25.
Wang L, Zhang J Y, Dong C T, et al. A Method of Applying Flight Data to Evaluate Landing Operation Performance[J]. Ergonomics, 2019, 62(2): 171-180.
Liu S, Zhang Y X, Chen J T: A System for Evaluating Pilot Performance Based on Flight Data, Harris D, editor, Engineering Psychology and Cognitive Ergonomics, Cham: Springer International Publishing Ag, 2018: 605-614.
SUN Rui-shan,XIAO Ya-bing. Research on indicating structure for operation characteristic of civilaviation pilots based on QAR data[J]. Journal of Safety Science and Technology, 2012, 8(11): 49-54.
CAO Huiling, GAO Jianzhong, LIANG Damin. Research on Emission Diffusion Model of Civil Aircraft Engine in Cruise [J]. Environmental Science & Technology, 2014, 37(S1): 444-447.
LI Chaoyi, SUN Jianzhong, YAN Hongsheng, et al. Estimation of exhausts pollution emissions for civil aircraft engine based on QAR data [J]. Chinese Journal of Environmental Engineering, 2017, 11(06): 3607-3616.
CAO Huiling, TANG Xinhao, MIAO Jiahe. Calculation and analysis of nitrogen oxide emission in LTO stage of engine based on QAR data [J]. Acta,Science Circumstantiae, 2018, 38(10): 3900-3904.
CAO Huiling, LI Yuming, TANG Xinhao. Calculation and analysis of black carbon emissions from aircraft full flight phasebased on QAR data [J]. Acta Science Circumstantiae 2020, 40(06): 1951-1957.
Alizadeh A, Uzun M, Koyuncu E, et al. Optimal En-Route Trajectory Planning based on Wind Information[J]. Ifac Papersonline, 2018, 51(9): 180-185.
Lv H, Yu J J, Zhu T Y, et al. A Novel Method of Overrun Risk Measurement and Assessment using Large Scale QAR Data[M]. Los Alamitos: Ieee Computer Soc, 2018: 213-220.
Sun Rui-shan, Yang Yi-xuan, Wang Lei. Study on flight safety evaluation based on QAR data[J]. China Safety Science Journal, 2015, 25(07): 87-92.
Fang F, Zhang R Q, Zhao X B. An Aggregated Evaluation and Multi-dimensional Comparison Method of Flight Safety Based on QAR Data[M]. New York: Ieee, 2020: 145-149.
Wang X, Zhao X B, Yu L L. Data Mining on the Flight Quality of an Airline based on QAR Big Data[M]. New York: Ieee, 2020: 955-958.









